WHAT IS LIVELABS? Government funded test- bed in urban locations - - PDF document

what is livelabs government funded test bed in urban
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WHAT IS LIVELABS? Government funded test- bed in urban locations - - PDF document

25/7/2014 The Challenge of Continuous Mobile Context Sensing Talk at COMSNETS 2014 Bengalaru, Jan 9 th 2014 WHAT IS LIVELABS? Government funded test- bed in urban locations Companies can run large scale experiments on REAL people in REAL


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25/7/2014 1

The Challenge of Continuous Mobile Context Sensing Talk at COMSNETS 2014 Bengalaru, Jan 9th 2014

WHAT IS LIVELABS? Government funded test- bed in urban locations

Companies can run large scale experiments on REAL people in REAL environments Focus on developing and testing context-aware urban applications & services

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25/7/2014 2

LIVELABS IN ACTION

30,000 opt-in consumers

Retail & Consumption Leisure & Tourism Telco & IDM

Multiple Urban Venues & Lifestyle Verticals

Mall@Singapore Sentosa Changi Airport SMU

Resource-efficient deep context collection Real-time mobile analytics & insights Real World experimentation

LIVELABS: PARTICIPANTS & VENUES

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BENEFITS/FOCUS OF EACH LIVELABS TESTBED

  • Fine-grained and long-term data

monitoring 5,000 committed users with 3-4 year longitudinal experimentation study Cellular + Wi-Fi

  • Unique leisure demographic mix

(families, tourists, and students) of 10,000 users

  • Mix of popular outdoor (beaches,

musical fountain, etc.) and indoor areas. Medium-capacity cellular+ WiFi network.

  • Large downtown mall testbed (~800K
  • sq. ft., > 50,000 visitors per day)

 Diverse mix of retailers & mix of youth & family demographics (movie theatre etc.) Medium-capacity WiFi network

  • Extremely busy airport – over 135,000

passengers per day

  • Logistics & Retail location
  • Two different groups of visitors ---

transit and visitors High-capacity Wi-Fi

LiveLabs@SMU LiveLabs@Sentosa LiveLabs@Plaza Sing LiveLabs@Changi Airport

LIVELABS DATA FLOW

Internet Cloud

Real-time Analytics Server Experimentation Server Results Server Investigators

LiveLabs Urban Lifestyle Innovation Platform

LiveLabs Context Collection application Installed in smart phones

External Analytics Providers

(eg. LARC, IBM, Accenture,..)

Specify Interventions

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25/7/2014 4

  • 1. Deep, energy-efficient,

continuous, context collection

  • 2. Continuous indoor location

tracking in public spaces

  • 3. Derive Deep Analytics from

Context

  • 4. Run automated social

experiments on mobile devices

  • 5. Handle transient network

traffic loads

LiveLabs: Key Component Technologies

  • Clients for Android, iOS, Phone8 .
  • Server-controlled capture of phone events

(e.g., SMS, URLs) & sensor data

  • Client-side +-3m accuracy for Android.

iOS

  • Client-side +-3m accuracy for Android.
  • Server-side tracking for all platforms (e.g.,

iOS, Phone 8)

  • Real-time Queue Detection System.
  • Detection of Dynamic Groups from

Spatiotemporal trajectories

  • Intervention Management Portal (v1)

ads/promotions.

  • Intervention Management Portal (v1)

allows location & time-based delivery of ads/promotions.

  • Use of TV Whitespace and real-time RF

Mapping technologies under investigation

Key Research Challenges Current Innovations/Capabilities

ACHIEVEMENTS

  • LiveLabs@SMU operational since Sep 2012.
  • Approx. 850 participants signed up; approx. 420 active participants
  • Data collection for Android and iOS platforms deployed
  • Campus-wide Indoor Location Tracking
  • Longitudinal traces of over 3000+ individual devices using server-side location
  • Controlled activation of fine-grained client-side location (Android)
  • Developed Analytics over Mobile Data
  • Queuing Detection: Research prototype tested
  • Group Detection: Under active R&D
  • Interventions/Promotions
  • Merchant promotions provided to participants via SMUddy App
  • Dynamic context-based promotions ready for demos
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25/7/2014 5

LIVELABS: LESSONS LEARNED UP TO NOW

  • Indoor Location Tracking is Not a Solved Problem
  • Too many real-world anomalies with existing techniques
  • The Tail Really Does Matter!
  • Venue operators prefer solutions with no fluctuation (even if base is worse)
  • Attracting Participants is Easy, Retention is Hard!
  • Need to find what motivates participants to stay on (apps in our case)
  • Production, Research, and Administration Do Not Mix!
  • Needed separate teams for each to ensure quality and prevent burnout
  • Cannot do Continuous Mobile Sensing
  • Large amounts of low fidelity sensing with burst of high fidelity sensing

THE CHALLENGE OF CONTINUOUS SENSING

1) Energy cost of individual sensors is large 2) Energy cost of multiple sensors may not be linear 3) Energy cost of multiple tasks is dominated by the most expensive taks

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ENERGY COST OF INDIVIDUAL SENSORS

20 40 60 80 100 120 140 160 slowest slow fast fastest

Power Consumption (mW) Sensing Rate (4 default modes on android) Accelerometer Gyroscope Compass IT GETS WORSE WITH PROCESSING & STORAGE!!

50 100 150 200 250 300 350 400 450 500 slowest slow fast fastest

Power Consumption (mW) Sensing Rate (4 default modes on android) Accel with Internal Flash Storage Accel w/o Internal Flash Storage Light with Internal Flash Storage Light w/o Internal Flash Storage 2x higher 5x higher!!

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ENERGY COSTS MAY NOT BE LINEAR

200 400 600 800 1000 1200 1400 slowest slow fast fastest

Power Consumption (mW) Sensing Rate (4 default modes on android) All inertial sensors (accel, gyro, compass) Inertial + location + others (pressure, light) no difference Large sub linear increase Large non linear increase OTHER CHALLENGES

1) Heterogeneity of devices

  • Different devices have different sensors
  • Energy costs, latencies, accuracies all differ

2) Accuracy is not the only important metric. Latency matters too!!

  • No point collecting accurate data 1 hr ago for a real-time application
  • Hence, transmission and computation costs must be factored in
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25/7/2014 8

SUMMARY

  • LiveLabs aims to change 3 real-world venues into living testbeds
  • Using the cell phones of opted in participants as the main sensors
  • Collecting sensor data from these phones in an energy-efficient yet accurate manner

is challenging

  • Current Solution
  • Low fidelity sensing by default with high fidelity sensing enabled for short periods

(duing expts)

FOR MORE DETAILS

Contact me at rajesh@smu.edu.sg and/or visit http://www.livelabs.smu.edu.sg We are looking to hire Post-docs, research engineers, and Ph.D. students (in all areas of systems development and research) Please contact me if you are interested.